Description: Recent innovations in model fitting technology allow ecologists to fit an impressive array of complex, hierarchical and parameter-rich models to data. Both Bayesians and frequentists alike can harness modern computational power to entertain and fit models of complexity that were out of practical reach less than a generation ago. With this model fitting capability in hand, a necessary next question to ask is: how does one choose the right level of model complexity for a given problem? The goal of this session is to bring together ecologists and applied statisticians who have recently grappled with questions of model complexity or parsimony in an ecological context. Model complexity and parsimony is a particularly interesting axis along which to frame a discussion of contemporary statistical frontiers because it is an issue confronted by all data analysts, and it impacts the interpretation of scientific results in major ways. However, different statistical paradigms have different philosophies concerning how model complexity affects the interpretation of an analysis and how one interprets model parsimony. An explicit goal of this session is to bring together speakers from a variety of statistical paradigms and to ask each to emphasize how their different vantage points may lead to different viewpoints on parsimony in statistical modeling. However, we aim to avoid frequentist versus Bayesian overtones, and towards this end speakers will be asked to address issues of complexity and parsimony within the context of their own statistical paradigm, and to refrain from commenting negatively on other paradigms. In addition to this more philosophical angle, the session will also emphasize practical strategies for data-driven model selection with real data analysis problems. Speakers will be asked to illustrate their viewpoints on model complexity in the context of a real ecological data analysis problem. Thus, we hope that this session will interest a practical audience, and provide a useful roadmap of the different philosophies and methodologies regarding model complexity and model selection.